# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# SPDX-License-Identifier: Apache-2.0

import argparse
import json


def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--annotation-file", type=str)
    parser.add_argument("--result-file", type=str)
    parser.add_argument("--result-upload-file", type=str)
    return parser.parse_args()


def eval_single(result_file, eval_only_type=None):
    results = {}
    for line in open(result_file):
        row = json.loads(line)
        results[row["question_id"]] = row

    type_counts = {}
    correct_counts = {}
    for question_data in data["questions"]:
        if eval_only_type is not None and question_data["data_type"] != eval_only_type:
            continue
        data_type = question_data["question_type_id"]
        type_counts[data_type] = type_counts.get(data_type, 0) + 1
        try:
            question_id = int(question_data["question_id"])
        except BaseException:
            question_id = question_data["question_id"]
        if question_id not in results:
            correct_counts[data_type] = correct_counts.get(data_type, 0)
            continue
        row = results[question_id]
        if row["text"] == question_data["answer"]:
            correct_counts[data_type] = correct_counts.get(data_type, 0) + 1

    total_count = 0
    total_correct = 0
    for data_type in sorted(type_counts.keys()):
        accuracy = correct_counts[data_type] / type_counts[data_type] * 100
        if eval_only_type is None:
            print(f"{ques_type_id_to_name[data_type]}: {accuracy:.2f}%")

        total_count += type_counts[data_type]
        total_correct += correct_counts[data_type]

    total_accuracy = total_correct / total_count * 100
    if eval_only_type is None:
        print(f"Total accuracy: {total_accuracy:.2f}%")
    else:
        print(f"{eval_only_type} accuracy: {total_accuracy:.2f}%")

    return results


if __name__ == "__main__":
    args = get_args()
    data = json.load(open(args.annotation_file))
    ques_type_id_to_name = {id: n for n, id in data["question_type"].items()}

    results = eval_single(args.result_file)
    eval_single(args.result_file, eval_only_type="image")
    eval_single(args.result_file, eval_only_type="video")

    with open(args.result_upload_file, "w") as fp:
        for question in data["questions"]:
            qid = question["question_id"]
            if qid in results:
                result = results[qid]
            else:
                result = results[int(qid)]
            fp.write(json.dumps({"question_id": qid, "prediction": result["text"]}) + "\n")
